Hands-on course on LLM security: learn prompt injection, jailbreaks, adversarial attacks, and defensive controls
β±οΈ Length: 1.3 total hours
β 4.57/5 rating
π₯ 1,580 students
π November 2025 update
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- Course Overview
- This intensive, hands-on course plunges participants into the dynamic realm of AI red teaming and Large Language Model (LLM) security, offering a robust foundation in identifying and exploiting vulnerabilities within advanced AI systems.
- Designed for cybersecurity professionals, ethical hackers, AI developers, and security-conscious individuals, it bridges the gap between theoretical AI knowledge and practical adversarial application.
- Through a meticulously crafted series of labs, learners will gain critical insights into the security landscape of LLMs, understanding how these powerful models can be manipulated or compromised.
- The curriculum emphasizes developing a proactive security mindset, enabling participants to anticipate potential attack vectors and contribute to the development of more resilient and trustworthy AI.
- Explore sophisticated techniques used by malicious actors, learning to deconstruct AI’s defensive layers and uncover hidden operational logic.
- This guide focuses on practical exploitation, moving beyond abstract concepts to deliver actionable skills for evaluating and enhancing AI safety.
- Participants will transform into proficient AI security evaluators, equipped to navigate the ethical challenges and technical complexities of securing cutting-edge artificial intelligence.
- The course reflects the urgent industry demand for experts capable of safeguarding AI, positioning learners at the forefront of this critical and evolving field.
- Requirements / Prerequisites
- A foundational grasp of core cybersecurity concepts, including common attack vectors and defense strategies, will greatly enhance the learning experience.
- Familiarity with command-line interfaces (CLI) and basic system administration tasks, particularly for setting up and managing local environments, is recommended.
- While not strictly mandatory, prior exposure to scripting languages, especially Python, will be beneficial for understanding some of the underlying tools and concepts.
- Access to a reliable internet connection and a personal computer capable of running virtualized environments (e.g., Docker, sufficient RAM/CPU) is essential for the lab components.
- An inquisitive and problem-solving mindset, coupled with a keen interest in exploring the boundaries and limitations of AI technologies, is highly encouraged.
- No advanced degrees or specialized certifications in AI/ML are required, as the course is structured to provide all necessary AI-specific security context from the ground up.
- A willingness to engage in hands-on, experimental learning, embracing the iterative nature of vulnerability discovery and exploitation.
- Skills Covered / Tools Used
- Adversarial Prompt Engineering Mastery: Develop advanced strategies for crafting prompts that bypass intended AI behaviors, leading to unauthorized information disclosure or control.
- AI System Vulnerability Assessment: Learn systematic methodologies to identify, categorize, and prioritize security weaknesses in Large Language Model deployments.
- Ethical Red Teaming Frameworks: Understand and apply established ethical red teaming principles specifically tailored for AI systems, ensuring responsible security testing practices.
- Containerized Security Lab Deployment: Gain proficiency in utilizing Docker and similar containerization technologies to build and manage secure, isolated environments for AI hacking exercises.
- Uncovering Hidden AI Configurations: Master techniques to probe and infer sensitive internal settings, proprietary instructions, or “meta-prompts” embedded within AI models without direct access.
- Defensive AI Control Evasion: Acquire sophisticated methods to circumvent and neutralize various guardrails, content filters, and safety mechanisms implemented in AI applications.
- Post-Exploitation Analysis for LLMs: Learn to interpret the results of successful attacks, understand their implications, and formulate recommendations for robust countermeasures.
- Secure LLM Development Best Practices: Develop an understanding of how to integrate security considerations throughout the lifecycle of AI model development and deployment.
- Tooling: Hands-on interaction with specialized AI red teaming platforms, Docker for environment setup, and simulated interactions with cloud-based LLM APIs (e.g., Azure OpenAI service components).
- Benefits / Outcomes
- Become a Certified AI Security Innovator: Emerge as a recognized expert in a niche, high-demand field, capable of tackling the evolving security challenges of AI.
- Accelerated Career Advancement: Position yourself uniquely in the cybersecurity market with highly sought-after skills in AI red teaming and LLM hacking, opening doors to specialized roles.
- Proactive Defense Strategist: Develop the critical ability to think like an attacker, enabling you to design and implement robust defensive strategies for AI systems before vulnerabilities are exploited.
- Master of Practical Application: Translate theoretical knowledge into tangible, repeatable skills through extensive lab work, ensuring you can immediately apply what youβve learned in real-world scenarios.
- Deepened AI Understanding: Gain an unparalleled understanding of LLM operational dynamics, internal logic, and failure modes, far beyond that of a typical user or developer.
- Contributor to Responsible AI: Play a pivotal role in advancing the safety and ethical deployment of artificial intelligence by identifying and mitigating critical security risks.
- Expanded Professional Network: Connect with a community of peers and instructors passionate about AI security, fostering collaboration and knowledge exchange.
- Cultivate an Adversarial Mindset: Hone your analytical and creative problem-solving skills, applying an attacker-centric perspective to uncover hidden weaknesses in complex AI architectures.
- PROS
- Intensely Practical and Lab-Focused: The course design is heavily biased towards hands-on exercises, ensuring immediate application and reinforcement of complex concepts.
- Highly Relevant and Current Content: Addresses the very latest vulnerabilities and red teaming techniques specific to cutting-edge LLMs, making the acquired skills highly pertinent to today’s AI landscape.
- Foundation for Personal AI Hacking Lab: Guides learners through the setup of their own secure, powerful, and reproducible AI red teaming environment, fostering continuous learning and experimentation.
- Real-World Simulation: Utilizes official platforms like the Microsoft AI Red Teaming Playground to simulate authentic industry-level challenges and scenarios.
- High Student Satisfaction and Credibility: A strong rating (4.57/5) from a significant number of students (1,580) indicates a well-regarded and effective learning experience.
- Focus on Unrestricted Testing: Encourages and teaches the safe and ethical deployment of uncensored models for comprehensive adversarial testing, going beyond standard limitations.
- CONS
- Condensed Format for Advanced Topics: While comprehensive, the 1.3-hour total duration for such advanced and practical topics might require significant self-paced practice and external research to fully master every nuanced technique presented.
Learning Tracks: English,Business,Operations
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